Overview

Dataset statistics

Number of variables29
Number of observations294896
Missing cells295390
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.2 MiB
Average record size in memory232.0 B

Variable types

Numeric17
Categorical5
Text6
DateTime1

Alerts

type is highly imbalanced (86.3%)Imbalance
engines is highly imbalanced (87.5%)Imbalance
engine is highly imbalanced (81.7%)Imbalance
faa has 5982 (2.0%) missing valuesMissing
name has 5982 (2.0%) missing valuesMissing
lat has 5982 (2.0%) missing valuesMissing
lon has 5982 (2.0%) missing valuesMissing
alt has 5982 (2.0%) missing valuesMissing
year_planes has 43853 (14.9%) missing valuesMissing
type has 36313 (12.3%) missing valuesMissing
manufacturer has 36313 (12.3%) missing valuesMissing
model has 36313 (12.3%) missing valuesMissing
engines has 36313 (12.3%) missing valuesMissing
seats has 36930 (12.5%) missing valuesMissing
engine has 36313 (12.3%) missing valuesMissing
year_planes is highly skewed (γ1 = -70.44976736)Skewed
dep_delay has 12586 (4.3%) zerosZeros
arr_delay has 4070 (1.4%) zerosZeros
wind_dir has 14366 (4.9%) zerosZeros
wind_speed has 14345 (4.9%) zerosZeros
wind_gust has 14345 (4.9%) zerosZeros

Reproduction

Analysis started2024-07-09 01:31:06.423177
Analysis finished2024-07-09 01:33:39.108999
Duration2 minutes and 32.69 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5567319
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:39.169481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4140719
Coefficient of variation (CV)0.5206972
Kurtosis-1.1824167
Mean6.5567319
Median Absolute Deviation (MAD)3
Skewness-0.014180706
Sum1933554
Variance11.655887
MonotonicityNot monotonic
2024-07-08T20:33:39.248374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 26377
8.9%
5 25433
8.6%
7 25415
8.6%
6 25288
8.6%
10 25221
8.6%
3 25088
8.5%
12 24810
8.4%
4 24691
8.4%
11 24243
8.2%
1 23769
8.1%
Other values (2) 44561
15.1%
ValueCountFrequency (%)
1 23769
8.1%
2 21387
7.3%
3 25088
8.5%
4 24691
8.4%
5 25433
8.6%
6 25288
8.6%
7 25415
8.6%
8 26377
8.9%
9 23174
7.9%
10 25221
8.6%
ValueCountFrequency (%)
12 24810
8.4%
11 24243
8.2%
10 25221
8.6%
9 23174
7.9%
8 26377
8.9%
7 25415
8.6%
6 25288
8.6%
5 25433
8.6%
4 24691
8.4%
3 25088
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.821788
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:39.329212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7932333
Coefficient of variation (CV)0.55576736
Kurtosis-1.1903546
Mean15.821788
Median Absolute Deviation (MAD)8
Skewness-0.010585658
Sum4665782
Variance77.320952
MonotonicityNot monotonic
2024-07-08T20:33:39.417281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
27 10229
 
3.5%
20 10196
 
3.5%
26 10138
 
3.4%
17 10103
 
3.4%
13 9966
 
3.4%
21 9933
 
3.4%
16 9907
 
3.4%
12 9882
 
3.4%
10 9834
 
3.3%
22 9803
 
3.3%
Other values (21) 194905
66.1%
ValueCountFrequency (%)
1 9731
3.3%
2 9447
3.2%
3 9661
3.3%
4 9261
3.1%
5 9658
3.3%
6 9779
3.3%
7 9415
3.2%
8 9671
3.3%
9 9000
3.1%
10 9834
3.3%
ValueCountFrequency (%)
31 5760
2.0%
30 9015
3.1%
29 8807
3.0%
28 9677
3.3%
27 10229
3.5%
26 10138
3.4%
25 9522
3.2%
24 9591
3.3%
23 9576
3.2%
22 9803
3.3%

dep_time
Real number (ℝ)

Distinct1384
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1346.8534
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:39.508741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile611
Q1905
median1348
Q31757
95-th percentile2133
Maximum2400
Range2399
Interquartile range (IQR)852

Descriptive statistics

Standard deviation505.93596
Coefficient of variation (CV)0.37564293
Kurtosis-1.0538176
Mean1346.8534
Median Absolute Deviation (MAD)440
Skewness-0.0046803658
Sum3.9718168 × 108
Variance255971.2
MonotonicityNot monotonic
2024-07-08T20:33:39.619138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 918
 
0.3%
556 905
 
0.3%
557 847
 
0.3%
655 812
 
0.3%
755 762
 
0.3%
554 758
 
0.3%
654 747
 
0.3%
656 742
 
0.3%
558 732
 
0.2%
657 710
 
0.2%
Other values (1374) 286963
97.3%
ValueCountFrequency (%)
1 33
< 0.1%
2 28
< 0.1%
3 36
< 0.1%
4 41
< 0.1%
5 47
< 0.1%
6 34
< 0.1%
7 26
< 0.1%
8 33
< 0.1%
9 23
< 0.1%
10 32
< 0.1%
ValueCountFrequency (%)
2400 28
< 0.1%
2359 49
< 0.1%
2358 47
< 0.1%
2357 66
< 0.1%
2356 53
< 0.1%
2355 61
< 0.1%
2354 52
< 0.1%
2353 68
< 0.1%
2352 55
< 0.1%
2351 60
< 0.1%

dep_delay
Real number (ℝ)

ZEROS 

Distinct759
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.083009
Minimum-84
Maximum1548
Zeros12586
Zeros (%)4.3%
Negative172763
Negative (%)58.6%
Memory size2.2 MiB
2024-07-08T20:33:39.717732image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-84
5-th percentile-10
Q1-6
median-2
Q311
95-th percentile103
Maximum1548
Range1632
Interquartile range (IQR)17

Descriptive statistics

Standard deviation51.793249
Coefficient of variation (CV)3.4338804
Kurtosis82.673609
Mean15.083009
Median Absolute Deviation (MAD)5
Skewness6.6561896
Sum4447919
Variance2682.5407
MonotonicityNot monotonic
2024-07-08T20:33:39.810499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 22518
 
7.6%
-4 21579
 
7.3%
-6 20245
 
6.9%
-3 19815
 
6.7%
-2 17597
 
6.0%
-7 16923
 
5.7%
-1 15167
 
5.1%
-8 12975
 
4.4%
0 12586
 
4.3%
-9 9232
 
3.1%
Other values (749) 126259
42.8%
ValueCountFrequency (%)
-84 1
 
< 0.1%
-29 1
 
< 0.1%
-28 2
 
< 0.1%
-27 1
 
< 0.1%
-26 1
 
< 0.1%
-25 3
 
< 0.1%
-24 13
 
< 0.1%
-23 16
< 0.1%
-22 17
< 0.1%
-21 34
< 0.1%
ValueCountFrequency (%)
1548 1
< 0.1%
1301 1
< 0.1%
1253 1
< 0.1%
1248 1
< 0.1%
1227 1
< 0.1%
1217 1
< 0.1%
1214 1
< 0.1%
1201 1
< 0.1%
1175 1
< 0.1%
1174 1
< 0.1%

arr_delay
Real number (ℝ)

ZEROS 

Distinct802
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1040231
Minimum-98
Maximum1589
Zeros4070
Zeros (%)1.4%
Negative179899
Negative (%)61.0%
Memory size2.2 MiB
2024-07-08T20:33:39.910169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-98
5-th percentile-36
Q1-20
median-7
Q313
95-th percentile102
Maximum1589
Range1687
Interquartile range (IQR)33

Descriptive statistics

Standard deviation55.323259
Coefficient of variation (CV)7.7875956
Kurtosis64.176321
Mean7.1040231
Median Absolute Deviation (MAD)15
Skewness5.5591971
Sum2094948
Variance3060.663
MonotonicityNot monotonic
2024-07-08T20:33:40.005720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 6118
 
2.1%
-13 6063
 
2.1%
-15 6000
 
2.0%
-17 5991
 
2.0%
-18 5971
 
2.0%
-19 5946
 
2.0%
-16 5909
 
2.0%
-12 5777
 
2.0%
-11 5727
 
1.9%
-10 5649
 
1.9%
Other values (792) 235745
79.9%
ValueCountFrequency (%)
-98 1
 
< 0.1%
-84 1
 
< 0.1%
-82 1
 
< 0.1%
-78 5
< 0.1%
-77 1
 
< 0.1%
-76 2
 
< 0.1%
-75 1
 
< 0.1%
-74 4
< 0.1%
-72 7
< 0.1%
-71 4
< 0.1%
ValueCountFrequency (%)
1589 1
< 0.1%
1289 1
< 0.1%
1276 1
< 0.1%
1270 1
< 0.1%
1250 1
< 0.1%
1235 1
< 0.1%
1219 1
< 0.1%
1187 1
< 0.1%
1171 1
< 0.1%
1170 1
< 0.1%

carrier
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
UA
62420 
DL
57753 
B6
56155 
AA
45484 
EV
36276 
Other values (7)
36808 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters589792
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUA
2nd rowUA
3rd rowB6
4th rowUA
5th rowNK

Common Values

ValueCountFrequency (%)
UA 62420
21.2%
DL 57753
19.6%
B6 56155
19.0%
AA 45484
15.4%
EV 36276
12.3%
WN 16736
 
5.7%
VX 7513
 
2.5%
NK 6432
 
2.2%
OO 2505
 
0.8%
AS 2312
 
0.8%
Other values (2) 1310
 
0.4%

Length

2024-07-08T20:33:40.096233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ua 62420
21.2%
dl 57753
19.6%
b6 56155
19.0%
aa 45484
15.4%
ev 36276
12.3%
wn 16736
 
5.7%
vx 7513
 
2.5%
nk 6432
 
2.2%
oo 2505
 
0.8%
as 2312
 
0.8%
Other values (2) 1310
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 156048
26.5%
U 62420
10.6%
D 57753
 
9.8%
L 57753
 
9.8%
B 56155
 
9.5%
6 56155
 
9.5%
V 43789
 
7.4%
E 36276
 
6.2%
N 23168
 
3.9%
W 16736
 
2.8%
Other values (7) 23539
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 589792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 156048
26.5%
U 62420
10.6%
D 57753
 
9.8%
L 57753
 
9.8%
B 56155
 
9.5%
6 56155
 
9.5%
V 43789
 
7.4%
E 36276
 
6.2%
N 23168
 
3.9%
W 16736
 
2.8%
Other values (7) 23539
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 589792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 156048
26.5%
U 62420
10.6%
D 57753
 
9.8%
L 57753
 
9.8%
B 56155
 
9.5%
6 56155
 
9.5%
V 43789
 
7.4%
E 36276
 
6.2%
N 23168
 
3.9%
W 16736
 
2.8%
Other values (7) 23539
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 589792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 156048
26.5%
U 62420
10.6%
D 57753
 
9.8%
L 57753
 
9.8%
B 56155
 
9.5%
6 56155
 
9.5%
V 43789
 
7.4%
E 36276
 
6.2%
N 23168
 
3.9%
W 16736
 
2.8%
Other values (7) 23539
 
4.0%

flight
Real number (ℝ)

Distinct3376
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1687.2966
Minimum1
Maximum6988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:40.186348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile108
Q1518
median1332
Q32227
95-th percentile5144
Maximum6988
Range6987
Interquartile range (IQR)1709

Descriptive statistics

Standard deviation1452.0065
Coefficient of variation (CV)0.860552
Kurtosis0.63110119
Mean1687.2966
Median Absolute Deviation (MAD)834
Skewness1.1666478
Sum4.9757702 × 108
Variance2108322.8
MonotonicityNot monotonic
2024-07-08T20:33:40.280502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211 856
 
0.3%
23 843
 
0.3%
1415 835
 
0.3%
1161 829
 
0.3%
1105 809
 
0.3%
149 794
 
0.3%
575 784
 
0.3%
581 778
 
0.3%
423 759
 
0.3%
523 716
 
0.2%
Other values (3366) 286893
97.3%
ValueCountFrequency (%)
1 707
0.2%
2 284
0.1%
3 567
0.2%
5 278
 
0.1%
7 361
0.1%
9 325
0.1%
11 600
0.2%
14 1
 
< 0.1%
15 562
0.2%
17 41
 
< 0.1%
ValueCountFrequency (%)
6988 11
< 0.1%
6899 12
< 0.1%
6889 12
< 0.1%
6879 2
 
< 0.1%
6876 1
 
< 0.1%
6875 4
 
< 0.1%
6860 1
 
< 0.1%
6853 5
< 0.1%
6834 12
< 0.1%
6816 7
< 0.1%
Distinct4122
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:40.451771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9961342
Min length5

Characters and Unicode

Total characters1768236
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)< 0.1%

Sample

1st rowN54711
2nd rowN37287
3rd rowN547JB
4th rowN38446
5th rowN675NK
ValueCountFrequency (%)
n751ev 453
 
0.2%
n723ev 435
 
0.1%
n758ev 432
 
0.1%
n391ca 430
 
0.1%
n752ev 426
 
0.1%
n730ev 424
 
0.1%
n761nd 420
 
0.1%
n720ev 410
 
0.1%
n791aa 403
 
0.1%
n710ev 401
 
0.1%
Other values (4112) 290662
98.6%
2024-07-08T20:33:40.701545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 337727
19.1%
A 144294
 
8.2%
3 130391
 
7.4%
7 115682
 
6.5%
1 110499
 
6.2%
9 106242
 
6.0%
5 98955
 
5.6%
6 93831
 
5.3%
2 92088
 
5.2%
4 89720
 
5.1%
Other values (24) 448807
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1768236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 337727
19.1%
A 144294
 
8.2%
3 130391
 
7.4%
7 115682
 
6.5%
1 110499
 
6.2%
9 106242
 
6.0%
5 98955
 
5.6%
6 93831
 
5.3%
2 92088
 
5.2%
4 89720
 
5.1%
Other values (24) 448807
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1768236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 337727
19.1%
A 144294
 
8.2%
3 130391
 
7.4%
7 115682
 
6.5%
1 110499
 
6.2%
9 106242
 
6.0%
5 98955
 
5.6%
6 93831
 
5.3%
2 92088
 
5.2%
4 89720
 
5.1%
Other values (24) 448807
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1768236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 337727
19.1%
A 144294
 
8.2%
3 130391
 
7.4%
7 115682
 
6.5%
1 110499
 
6.2%
9 106242
 
6.0%
5 98955
 
5.6%
6 93831
 
5.3%
2 92088
 
5.2%
4 89720
 
5.1%
Other values (24) 448807
25.4%

origin
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
EWR
112702 
JFK
92055 
LGA
90139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters884688
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWR
2nd rowEWR
3rd rowEWR
4th rowLGA
5th rowEWR

Common Values

ValueCountFrequency (%)
EWR 112702
38.2%
JFK 92055
31.2%
LGA 90139
30.6%

Length

2024-07-08T20:33:40.812379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-08T20:33:40.912512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
ewr 112702
38.2%
jfk 92055
31.2%
lga 90139
30.6%

Most occurring characters

ValueCountFrequency (%)
E 112702
12.7%
W 112702
12.7%
R 112702
12.7%
J 92055
10.4%
F 92055
10.4%
K 92055
10.4%
L 90139
10.2%
G 90139
10.2%
A 90139
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 884688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 112702
12.7%
W 112702
12.7%
R 112702
12.7%
J 92055
10.4%
F 92055
10.4%
K 92055
10.4%
L 90139
10.2%
G 90139
10.2%
A 90139
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 884688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 112702
12.7%
W 112702
12.7%
R 112702
12.7%
J 92055
10.4%
F 92055
10.4%
K 92055
10.4%
L 90139
10.2%
G 90139
10.2%
A 90139
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 884688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 112702
12.7%
W 112702
12.7%
R 112702
12.7%
J 92055
10.4%
F 92055
10.4%
K 92055
10.4%
L 90139
10.2%
G 90139
10.2%
A 90139
10.2%

dest
Text

Distinct109
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:41.044136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters884688
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIA
2nd rowIAH
3rd rowMCO
4th rowORD
5th rowFLL
ValueCountFrequency (%)
lax 17833
 
6.0%
atl 16468
 
5.6%
ord 16249
 
5.5%
mco 16040
 
5.4%
bos 15322
 
5.2%
fll 14988
 
5.1%
sfo 13927
 
4.7%
mia 12246
 
4.2%
clt 10282
 
3.5%
den 8423
 
2.9%
Other values (99) 153118
51.9%
2024-07-08T20:33:41.272409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 97340
 
11.0%
L 97025
 
11.0%
S 83870
 
9.5%
O 68777
 
7.8%
D 56664
 
6.4%
M 51371
 
5.8%
C 49531
 
5.6%
T 48638
 
5.5%
F 42084
 
4.8%
B 35296
 
4.0%
Other values (16) 254092
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 884688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 97340
 
11.0%
L 97025
 
11.0%
S 83870
 
9.5%
O 68777
 
7.8%
D 56664
 
6.4%
M 51371
 
5.8%
C 49531
 
5.6%
T 48638
 
5.5%
F 42084
 
4.8%
B 35296
 
4.0%
Other values (16) 254092
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 884688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 97340
 
11.0%
L 97025
 
11.0%
S 83870
 
9.5%
O 68777
 
7.8%
D 56664
 
6.4%
M 51371
 
5.8%
C 49531
 
5.6%
T 48638
 
5.5%
F 42084
 
4.8%
B 35296
 
4.0%
Other values (16) 254092
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 884688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 97340
 
11.0%
L 97025
 
11.0%
S 83870
 
9.5%
O 68777
 
7.8%
D 56664
 
6.4%
M 51371
 
5.8%
C 49531
 
5.6%
T 48638
 
5.5%
F 42084
 
4.8%
B 35296
 
4.0%
Other values (16) 254092
28.7%

air_time
Real number (ℝ)

Distinct524
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.48943
Minimum20
Maximum712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:41.377078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile41
Q199
median139
Q3211
95-th percentile344
Maximum712
Range692
Interquartile range (IQR)112

Descriptive statistics

Standard deviation97.872896
Coefficient of variation (CV)0.59501026
Kurtosis0.49227987
Mean164.48943
Median Absolute Deviation (MAD)53
Skewness0.92941677
Sum48507274
Variance9579.1038
MonotonicityNot monotonic
2024-07-08T20:33:41.468615image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 2592
 
0.9%
144 2544
 
0.9%
145 2495
 
0.8%
147 2470
 
0.8%
146 2456
 
0.8%
142 2441
 
0.8%
141 2430
 
0.8%
148 2318
 
0.8%
140 2283
 
0.8%
149 2200
 
0.7%
Other values (514) 270667
91.8%
ValueCountFrequency (%)
20 1
 
< 0.1%
21 11
 
< 0.1%
22 20
 
< 0.1%
23 59
< 0.1%
24 58
< 0.1%
25 65
< 0.1%
26 76
< 0.1%
27 75
< 0.1%
28 115
< 0.1%
29 110
< 0.1%
ValueCountFrequency (%)
712 1
 
< 0.1%
711 1
 
< 0.1%
706 1
 
< 0.1%
704 1
 
< 0.1%
700 1
 
< 0.1%
698 1
 
< 0.1%
696 2
< 0.1%
693 1
 
< 0.1%
690 1
 
< 0.1%
688 3
< 0.1%

distance
Real number (ℝ)

Distinct204
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1160.3165
Minimum93
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:41.565328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile199
Q1636
median1005
Q31598
95-th percentile2565
Maximum4983
Range4890
Interquartile range (IQR)962

Descriptive statistics

Standard deviation762.67591
Coefficient of variation (CV)0.65729991
Kurtosis0.66821679
Mean1160.3165
Median Absolute Deviation (MAD)436
Skewness0.93187295
Sum3.4217268 × 108
Variance581674.54
MonotonicityNot monotonic
2024-07-08T20:33:41.658859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2475 12604
 
4.3%
733 10035
 
3.4%
762 8297
 
2.8%
2586 7741
 
2.6%
937 7081
 
2.4%
184 6232
 
2.1%
2454 6194
 
2.1%
2565 6186
 
2.1%
1065 5957
 
2.0%
1096 5902
 
2.0%
Other values (194) 218667
74.2%
ValueCountFrequency (%)
93 208
 
0.1%
94 223
 
0.1%
96 397
 
0.1%
116 271
 
0.1%
143 67
 
< 0.1%
160 498
 
0.2%
169 487
 
0.2%
173 156
 
0.1%
184 6232
2.1%
187 4362
1.5%
ValueCountFrequency (%)
4983 418
 
0.1%
4962 360
 
0.1%
3370 13
 
< 0.1%
2586 7741
2.6%
2576 298
 
0.1%
2569 302
 
0.1%
2565 6186
2.1%
2555 69
 
< 0.1%
2548 575
 
0.2%
2521 310
 
0.1%

faa
Text

MISSING 

Distinct105
Distinct (%)< 0.1%
Missing5982
Missing (%)2.0%
Memory size2.2 MiB
2024-07-08T20:33:41.793861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters866742
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIA
2nd rowIAH
3rd rowMCO
4th rowORD
5th rowFLL
ValueCountFrequency (%)
lax 17833
 
6.2%
atl 16468
 
5.7%
ord 16249
 
5.6%
mco 16040
 
5.6%
bos 15322
 
5.3%
fll 14988
 
5.2%
sfo 13927
 
4.8%
mia 12246
 
4.2%
clt 10282
 
3.6%
den 8423
 
2.9%
Other values (95) 147136
50.9%
2024-07-08T20:33:42.017415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 97340
11.2%
L 97025
 
11.2%
S 78590
 
9.1%
O 68777
 
7.9%
D 56664
 
6.5%
M 51371
 
5.9%
C 49531
 
5.7%
T 47424
 
5.5%
F 42084
 
4.9%
B 34594
 
4.0%
Other values (16) 243342
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 866742
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 97340
11.2%
L 97025
 
11.2%
S 78590
 
9.1%
O 68777
 
7.9%
D 56664
 
6.5%
M 51371
 
5.9%
C 49531
 
5.7%
T 47424
 
5.5%
F 42084
 
4.9%
B 34594
 
4.0%
Other values (16) 243342
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 866742
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 97340
11.2%
L 97025
 
11.2%
S 78590
 
9.1%
O 68777
 
7.9%
D 56664
 
6.5%
M 51371
 
5.9%
C 49531
 
5.7%
T 47424
 
5.5%
F 42084
 
4.9%
B 34594
 
4.0%
Other values (16) 243342
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 866742
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 97340
11.2%
L 97025
 
11.2%
S 78590
 
9.1%
O 68777
 
7.9%
D 56664
 
6.5%
M 51371
 
5.9%
C 49531
 
5.7%
T 47424
 
5.5%
F 42084
 
4.9%
B 34594
 
4.0%
Other values (16) 243342
28.1%

name
Text

MISSING 

Distinct105
Distinct (%)< 0.1%
Missing5982
Missing (%)2.0%
Memory size2.2 MiB
2024-07-08T20:33:42.181544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length60
Median length47
Mean length37.071083
Min length12

Characters and Unicode

Total characters10710355
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiami International Airport
2nd rowGeorge Bush Intercontinental Houston Airport
3rd rowOrlando International Airport
4th rowChicago O'Hare International Airport
5th rowFort Lauderdale Hollywood International Airport
ValueCountFrequency (%)
airport 284023
23.8%
international 254588
21.3%
fort 22490
 
1.9%
chicago 20139
 
1.7%
san 19008
 
1.6%
angeles 17833
 
1.5%
los 17833
 
1.5%
general 17196
 
1.4%
jackson 16561
 
1.4%
atlanta 16468
 
1.4%
Other values (190) 506345
42.5%
2024-07-08T20:33:42.449454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1132185
10.6%
n 1109351
10.4%
t 1054724
9.8%
a 1045908
9.8%
o 914752
8.5%
903570
 
8.4%
i 746649
 
7.0%
e 630750
 
5.9%
l 560437
 
5.2%
A 330534
 
3.1%
Other values (41) 2281495
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10710355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1132185
10.6%
n 1109351
10.4%
t 1054724
9.8%
a 1045908
9.8%
o 914752
8.5%
903570
 
8.4%
i 746649
 
7.0%
e 630750
 
5.9%
l 560437
 
5.2%
A 330534
 
3.1%
Other values (41) 2281495
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10710355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1132185
10.6%
n 1109351
10.4%
t 1054724
9.8%
a 1045908
9.8%
o 914752
8.5%
903570
 
8.4%
i 746649
 
7.0%
e 630750
 
5.9%
l 560437
 
5.2%
A 330534
 
3.1%
Other values (41) 2281495
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10710355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1132185
10.6%
n 1109351
10.4%
t 1054724
9.8%
a 1045908
9.8%
o 914752
8.5%
903570
 
8.4%
i 746649
 
7.0%
e 630750
 
5.9%
l 560437
 
5.2%
A 330534
 
3.1%
Other values (41) 2281495
21.3%

lat
Real number (ℝ)

MISSING 

Distinct105
Distinct (%)< 0.1%
Missing5982
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean35.379215
Minimum21.318701
Maximum61.1744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:42.558989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum21.318701
5-th percentile26.072599
Q129.993401
median35.214001
Q341.411701
95-th percentile43.646198
Maximum61.1744
Range39.8557
Interquartile range (IQR)11.418301

Descriptive statistics

Standard deviation5.9931986
Coefficient of variation (CV)0.16939885
Kurtosis-1.0651871
Mean35.379215
Median Absolute Deviation (MAD)5.2775001
Skewness-0.076027224
Sum10221551
Variance35.918429
MonotonicityNot monotonic
2024-07-08T20:33:42.654070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.94250107 17833
 
6.0%
33.63669968 16468
 
5.6%
41.97859955 16249
 
5.5%
28.42939949 16040
 
5.4%
42.36429977 15322
 
5.2%
26.07259941 14988
 
5.1%
37.61899948 13927
 
4.7%
25.79319954 12246
 
4.2%
35.2140007 10282
 
3.5%
39.86169815 8423
 
2.9%
Other values (95) 147136
49.9%
ValueCountFrequency (%)
21.31870079 778
 
0.3%
25.79319954 12246
4.2%
26.07259941 14988
5.1%
26.53619957 3490
 
1.2%
26.68320084 7679
2.6%
27.39539909 612
 
0.2%
27.97550011 7024
2.4%
28.42939949 16040
5.4%
29.179899 354
 
0.1%
29.53370094 618
 
0.2%
ValueCountFrequency (%)
61.17440033 13
 
< 0.1%
47.44900131 4495
1.5%
45.77750015 73
 
< 0.1%
45.58869934 1850
 
0.6%
44.88199997 4982
1.7%
44.80739975 474
 
0.2%
44.74140167 87
 
< 0.1%
44.47190094 2048
0.7%
43.64619827 2301
0.8%
43.6072998 93
 
< 0.1%

lon
Real number (ℝ)

MISSING 

Distinct105
Distinct (%)< 0.1%
Missing5982
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-91.264464
Minimum-157.922
Maximum-68.828102
Zeros0
Zeros (%)0.0%
Negative288914
Negative (%)98.0%
Memory size2.2 MiB
2024-07-08T20:33:42.762369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-157.922
5-th percentile-122.375
Q1-97.669899
median-84.428101
Q3-80.290604
95-th percentile-71.005203
Maximum-68.828102
Range89.093895
Interquartile range (IQR)17.379295

Descriptive statistics

Standard deviation16.140859
Coefficient of variation (CV)-0.17685809
Kurtosis-0.046954054
Mean-91.264464
Median Absolute Deviation (MAD)5.6406021
Skewness-0.98162803
Sum-26367581
Variance260.52733
MonotonicityNot monotonic
2024-07-08T20:33:42.853873image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.4079971 17833
 
6.0%
-84.42810059 16468
 
5.6%
-87.90480042 16249
 
5.5%
-81.30899811 16040
 
5.4%
-71.00520325 15322
 
5.2%
-80.15270233 14988
 
5.1%
-122.375 13927
 
4.7%
-80.29060364 12246
 
4.2%
-80.94309998 10282
 
3.5%
-104.6729965 8423
 
2.9%
Other values (95) 147136
49.9%
ValueCountFrequency (%)
-157.9219971 778
 
0.3%
-149.9960022 13
 
< 0.1%
-122.5979996 1850
 
0.6%
-122.375 13927
4.7%
-122.3089981 4495
 
1.5%
-122.2210007 367
 
0.1%
-121.9290009 877
 
0.3%
-121.5910034 515
 
0.2%
-119.7679977 293
 
0.1%
-118.4079971 17833
6.0%
ValueCountFrequency (%)
-68.82810211 474
 
0.2%
-70.06020355 416
 
0.1%
-70.28040314 93
 
< 0.1%
-70.30930328 2301
 
0.8%
-70.61430359 156
 
0.1%
-71.00520325 15322
5.2%
-71.42040253 498
 
0.2%
-71.43569946 349
 
0.1%
-72.68319702 271
 
0.1%
-73.15329742 2048
 
0.7%

alt
Real number (ℝ)

MISSING 

Distinct98
Distinct (%)< 0.1%
Missing5982
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean600.89314
Minimum4
Maximum6606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:42.945717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q120
median125
Q3748
95-th percentile2181
Maximum6606
Range6602
Interquartile range (IQR)728

Descriptive statistics

Standard deviation1062.124
Coefficient of variation (CV)1.7675755
Kurtosis12.657088
Mean600.89314
Median Absolute Deviation (MAD)117
Skewness3.4787435
Sum1.7360644 × 108
Variance1128107.4
MonotonicityNot monotonic
2024-07-08T20:33:43.037187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 17833
 
6.0%
1026 17242
 
5.8%
672 16249
 
5.5%
96 16040
 
5.4%
9 15355
 
5.2%
20 15322
 
5.2%
13 14705
 
5.0%
8 12246
 
4.2%
748 10282
 
3.5%
5431 8423
 
2.9%
Other values (88) 145217
49.2%
ValueCountFrequency (%)
4 2765
 
0.9%
8 12246
4.2%
9 15355
5.2%
13 14705
5.0%
15 3667
 
1.2%
17 3586
 
1.2%
19 7679
2.6%
20 15322
5.2%
25 1304
 
0.4%
26 8215
2.8%
ValueCountFrequency (%)
6606 24
 
< 0.1%
6548 203
 
0.1%
6451 93
 
< 0.1%
5759 40
 
< 0.1%
5431 8423
2.9%
5355 308
 
0.1%
4473 73
 
< 0.1%
4415 293
 
0.1%
4227 2635
 
0.9%
2643 121
 
< 0.1%

year_planes
Real number (ℝ)

MISSING  SKEWED 

Distinct59
Distinct (%)< 0.1%
Missing43853
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean2002.6253
Minimum0
Maximum2017
Zeros37
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:43.142189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1991
Q11999
median2002
Q32008
95-th percentile2015
Maximum2017
Range2017
Interquartile range (IQR)9

Descriptive statistics

Standard deviation25.616935
Coefficient of variation (CV)0.012791676
Kurtosis5502.0562
Mean2002.6253
Median Absolute Deviation (MAD)4
Skewness-70.449767
Sum5.0274507 × 108
Variance656.22737
MonotonicityNot monotonic
2024-07-08T20:33:43.246582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2001 23290
 
7.9%
2002 19993
 
6.8%
1999 17148
 
5.8%
2000 16687
 
5.7%
2004 14298
 
4.8%
1998 13277
 
4.5%
2006 12876
 
4.4%
2005 11503
 
3.9%
2003 11376
 
3.9%
2015 9831
 
3.3%
Other values (49) 100764
34.2%
(Missing) 43853
14.9%
ValueCountFrequency (%)
0 37
 
< 0.1%
1954 28
 
< 0.1%
1956 38
 
< 0.1%
1958 39
 
< 0.1%
1959 470
0.2%
1960 9
 
< 0.1%
1961 758
0.3%
1963 21
 
< 0.1%
1964 87
 
< 0.1%
1966 16
 
< 0.1%
ValueCountFrequency (%)
2017 3799
 
1.3%
2016 6869
2.3%
2015 9831
3.3%
2014 7971
2.7%
2013 8187
2.8%
2012 5388
1.8%
2011 5394
1.8%
2010 3344
 
1.1%
2009 4832
1.6%
2008 7735
2.6%

type
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing36313
Missing (%)12.3%
Memory size2.2 MiB
Fixed wing multi engine
247726 
Fixed wing single engine
 
9553
Rotorcraft
 
1244
Weight-shift-control
 
60

Length

Max length24
Median length23
Mean length22.973707
Min length10

Characters and Unicode

Total characters5940610
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFixed wing multi engine
2nd rowFixed wing multi engine
3rd rowFixed wing multi engine
4th rowFixed wing multi engine
5th rowFixed wing multi engine

Common Values

ValueCountFrequency (%)
Fixed wing multi engine 247726
84.0%
Fixed wing single engine 9553
 
3.2%
Rotorcraft 1244
 
0.4%
Weight-shift-control 60
 
< 0.1%
(Missing) 36313
 
12.3%

Length

2024-07-08T20:33:43.342028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-08T20:33:43.439152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
fixed 257279
25.0%
wing 257279
25.0%
engine 257279
25.0%
multi 247726
24.0%
single 9553
 
0.9%
rotorcraft 1244
 
0.1%
weight-shift-control 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 1029236
17.3%
n 781450
13.2%
e 781450
13.2%
771837
13.0%
g 524171
8.8%
l 257339
 
4.3%
F 257279
 
4.3%
w 257279
 
4.3%
d 257279
 
4.3%
x 257279
 
4.3%
Other values (13) 766011
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5940610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1029236
17.3%
n 781450
13.2%
e 781450
13.2%
771837
13.0%
g 524171
8.8%
l 257339
 
4.3%
F 257279
 
4.3%
w 257279
 
4.3%
d 257279
 
4.3%
x 257279
 
4.3%
Other values (13) 766011
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5940610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1029236
17.3%
n 781450
13.2%
e 781450
13.2%
771837
13.0%
g 524171
8.8%
l 257339
 
4.3%
F 257279
 
4.3%
w 257279
 
4.3%
d 257279
 
4.3%
x 257279
 
4.3%
Other values (13) 766011
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5940610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1029236
17.3%
n 781450
13.2%
e 781450
13.2%
771837
13.0%
g 524171
8.8%
l 257339
 
4.3%
F 257279
 
4.3%
w 257279
 
4.3%
d 257279
 
4.3%
x 257279
 
4.3%
Other values (13) 766011
12.9%

manufacturer
Text

MISSING 

Distinct73
Distinct (%)< 0.1%
Missing36313
Missing (%)12.3%
Memory size2.2 MiB
2024-07-08T20:33:43.536080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length29
Median length6
Mean length8.7135195
Min length3

Characters and Unicode

Total characters2253168
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOEING
2nd rowBOEING
3rd rowAIRBUS
4th rowBOEING
5th rowAIRBUS
ValueCountFrequency (%)
boeing 100799
29.8%
airbus 79034
23.4%
embraer 42133
12.5%
industrie 30552
 
9.0%
inc 15571
 
4.6%
bombardier 13864
 
4.1%
douglas 7762
 
2.3%
mcdonnell 7712
 
2.3%
aircraft 6345
 
1.9%
co 4743
 
1.4%
Other values (112) 29191
 
8.6%
2024-07-08T20:33:43.749211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 286254
12.7%
E 254669
11.3%
B 254134
11.3%
R 251069
11.1%
N 172152
7.6%
A 169721
7.5%
O 145751
6.5%
S 132461
 
5.9%
U 121683
 
5.4%
G 112141
 
5.0%
Other values (19) 353133
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2253168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 286254
12.7%
E 254669
11.3%
B 254134
11.3%
R 251069
11.1%
N 172152
7.6%
A 169721
7.5%
O 145751
6.5%
S 132461
 
5.9%
U 121683
 
5.4%
G 112141
 
5.0%
Other values (19) 353133
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2253168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 286254
12.7%
E 254669
11.3%
B 254134
11.3%
R 251069
11.1%
N 172152
7.6%
A 169721
7.5%
O 145751
6.5%
S 132461
 
5.9%
U 121683
 
5.4%
G 112141
 
5.0%
Other values (19) 353133
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2253168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 286254
12.7%
E 254669
11.3%
B 254134
11.3%
R 251069
11.1%
N 172152
7.6%
A 169721
7.5%
O 145751
6.5%
S 132461
 
5.9%
U 121683
 
5.4%
G 112141
 
5.0%
Other values (19) 353133
15.7%

model
Text

MISSING 

Distinct222
Distinct (%)0.1%
Missing36313
Missing (%)12.3%
Memory size2.2 MiB
2024-07-08T20:33:43.913951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.339682
Min length2

Characters and Unicode

Total characters2156500
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row737-724
2nd row737-824
3rd rowA320-232
4th row737-924ER
5th rowA321-231
ValueCountFrequency (%)
a320-232 39397
 
13.1%
erj 18888
 
6.3%
emb-145lr 17678
 
5.9%
190-100 17629
 
5.9%
igw 17629
 
5.9%
737-824 14301
 
4.8%
cl-600-2c10 13324
 
4.4%
717-200 11842
 
3.9%
737-7h4 10744
 
3.6%
737-924er 10639
 
3.5%
Other values (251) 128678
42.8%
2024-07-08T20:33:44.197786image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 305994
14.2%
- 265855
12.3%
3 228280
10.6%
7 227091
10.5%
0 199028
9.2%
1 166580
 
7.7%
4 92089
 
4.3%
A 85721
 
4.0%
R 67229
 
3.1%
E 62960
 
2.9%
Other values (32) 455673
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2156500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 305994
14.2%
- 265855
12.3%
3 228280
10.6%
7 227091
10.5%
0 199028
9.2%
1 166580
 
7.7%
4 92089
 
4.3%
A 85721
 
4.0%
R 67229
 
3.1%
E 62960
 
2.9%
Other values (32) 455673
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2156500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 305994
14.2%
- 265855
12.3%
3 228280
10.6%
7 227091
10.5%
0 199028
9.2%
1 166580
 
7.7%
4 92089
 
4.3%
A 85721
 
4.0%
R 67229
 
3.1%
E 62960
 
2.9%
Other values (32) 455673
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2156500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 305994
14.2%
- 265855
12.3%
3 228280
10.6%
7 227091
10.5%
0 199028
9.2%
1 166580
 
7.7%
4 92089
 
4.3%
A 85721
 
4.0%
R 67229
 
3.1%
E 62960
 
2.9%
Other values (32) 455673
21.1%

engines
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing36313
Missing (%)12.3%
Memory size2.2 MiB
2.0
247181 
1.0
 
10210
3.0
 
537
8.0
 
375
4.0
 
280

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters775749
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 247181
83.8%
1.0 10210
 
3.5%
3.0 537
 
0.2%
8.0 375
 
0.1%
4.0 280
 
0.1%
(Missing) 36313
 
12.3%

Length

2024-07-08T20:33:44.303725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-08T20:33:44.388530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 247181
95.6%
1.0 10210
 
3.9%
3.0 537
 
0.2%
8.0 375
 
0.1%
4.0 280
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 258583
33.3%
0 258583
33.3%
2 247181
31.9%
1 10210
 
1.3%
3 537
 
0.1%
8 375
 
< 0.1%
4 280
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 775749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 258583
33.3%
0 258583
33.3%
2 247181
31.9%
1 10210
 
1.3%
3 537
 
0.1%
8 375
 
< 0.1%
4 280
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 775749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 258583
33.3%
0 258583
33.3%
2 247181
31.9%
1 10210
 
1.3%
3 537
 
0.1%
8 375
 
< 0.1%
4 280
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 775749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 258583
33.3%
0 258583
33.3%
2 247181
31.9%
1 10210
 
1.3%
3 537
 
0.1%
8 375
 
< 0.1%
4 280
 
< 0.1%

seats
Real number (ℝ)

MISSING 

Distinct60
Distinct (%)< 0.1%
Missing36930
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean148.5976
Minimum1
Maximum552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:44.483163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q180
median149
Q3191
95-th percentile330
Maximum552
Range551
Interquartile range (IQR)111

Descriptive statistics

Standard deviation87.148223
Coefficient of variation (CV)0.58647128
Kurtosis1.2062449
Mean148.5976
Median Absolute Deviation (MAD)51
Skewness0.63020893
Sum38333128
Variance7594.8128
MonotonicityNot monotonic
2024-07-08T20:33:44.586367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 39397
13.4%
149 26412
 
9.0%
55 23263
 
7.9%
20 17667
 
6.0%
178 17553
 
6.0%
182 16904
 
5.7%
80 13324
 
4.5%
100 12674
 
4.3%
140 12300
 
4.2%
191 10747
 
3.6%
Other values (50) 67725
23.0%
(Missing) 36930
12.5%
ValueCountFrequency (%)
1 699
 
0.2%
2 2399
0.8%
4 4392
1.5%
5 1043
 
0.4%
6 1365
 
0.5%
7 1213
 
0.4%
8 685
 
0.2%
9 80
 
< 0.1%
10 615
 
0.2%
11 501
 
0.2%
ValueCountFrequency (%)
552 276
 
0.1%
451 117
 
< 0.1%
442 38
 
< 0.1%
440 10
 
< 0.1%
422 57
 
< 0.1%
400 1535
 
0.5%
379 10319
3.5%
377 348
 
0.1%
330 2974
 
1.0%
300 613
 
0.2%

engine
Categorical

IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing36313
Missing (%)12.3%
Memory size2.2 MiB
Turbo-fan
237682 
Reciprocating
 
9055
Turbo-jet
 
8321
Turbo-prop
 
2005
Electric
 
617
Other values (3)
 
903

Length

Max length13
Median length9
Mean length9.1433582
Min length7

Characters and Unicode

Total characters2364317
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTurbo-fan
2nd rowTurbo-fan
3rd rowTurbo-fan
4th rowTurbo-fan
5th rowTurbo-fan

Common Values

ValueCountFrequency (%)
Turbo-fan 237682
80.6%
Reciprocating 9055
 
3.1%
Turbo-jet 8321
 
2.8%
Turbo-prop 2005
 
0.7%
Electric 617
 
0.2%
4 Cycle 526
 
0.2%
Turbo-shaft 317
 
0.1%
2 Cycle 60
 
< 0.1%
(Missing) 36313
 
12.3%

Length

2024-07-08T20:33:44.677367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-08T20:33:44.779270image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
turbo-fan 237682
91.7%
reciprocating 9055
 
3.5%
turbo-jet 8321
 
3.2%
turbo-prop 2005
 
0.8%
electric 617
 
0.2%
cycle 586
 
0.2%
4 526
 
0.2%
turbo-shaft 317
 
0.1%
2 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 260002
11.0%
o 259385
11.0%
T 248325
10.5%
u 248325
10.5%
b 248325
10.5%
- 248325
10.5%
a 247054
10.4%
n 246737
10.4%
f 237999
10.1%
c 19930
 
0.8%
Other values (16) 99910
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2364317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 260002
11.0%
o 259385
11.0%
T 248325
10.5%
u 248325
10.5%
b 248325
10.5%
- 248325
10.5%
a 247054
10.4%
n 246737
10.4%
f 237999
10.1%
c 19930
 
0.8%
Other values (16) 99910
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2364317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 260002
11.0%
o 259385
11.0%
T 248325
10.5%
u 248325
10.5%
b 248325
10.5%
- 248325
10.5%
a 247054
10.4%
n 246737
10.4%
f 237999
10.1%
c 19930
 
0.8%
Other values (16) 99910
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2364317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 260002
11.0%
o 259385
11.0%
T 248325
10.5%
u 248325
10.5%
b 248325
10.5%
- 248325
10.5%
a 247054
10.4%
n 246737
10.4%
f 237999
10.1%
c 19930
 
0.8%
Other values (16) 99910
 
4.2%
Distinct7741
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Minimum2017-01-01 05:00:00
Maximum2018-01-01 00:00:00
2024-07-08T20:33:44.876504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:44.969606image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

wind_dir
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)< 0.1%
Missing783
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean186.84856
Minimum0
Maximum360
Zeros14366
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:45.063813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q180
median210
Q3280
95-th percentile340
Maximum360
Range360
Interquartile range (IQR)200

Descriptive statistics

Standard deviation111.27909
Coefficient of variation (CV)0.59555766
Kurtosis-1.1996557
Mean186.84856
Median Absolute Deviation (MAD)90
Skewness-0.25922332
Sum54954590
Variance12383.036
MonotonicityNot monotonic
2024-07-08T20:33:45.152511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 14366
 
4.9%
230 12475
 
4.2%
20 11534
 
3.9%
310 11032
 
3.7%
260 10987
 
3.7%
220 10922
 
3.7%
180 10553
 
3.6%
320 10332
 
3.5%
240 10099
 
3.4%
190 10033
 
3.4%
Other values (27) 181780
61.6%
ValueCountFrequency (%)
0 14366
4.9%
10 9333
3.2%
20 11534
3.9%
30 8705
3.0%
40 8330
2.8%
50 7382
2.5%
60 7337
2.5%
70 5542
 
1.9%
80 4150
 
1.4%
90 3864
 
1.3%
ValueCountFrequency (%)
360 8002
2.7%
350 6600
2.2%
340 7669
2.6%
330 9025
3.1%
320 10332
3.5%
310 11032
3.7%
300 9170
3.1%
290 7575
2.6%
280 7545
2.6%
270 8748
3.0%

wind_speed
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing783
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean9.5749668
Minimum0
Maximum32.22184
Zeros14345
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:45.240740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.45234
Q15.7539
median9.20624
Q312.65858
95-th percentile18.41248
Maximum32.22184
Range32.22184
Interquartile range (IQR)6.90468

Descriptive statistics

Standard deviation4.9011878
Coefficient of variation (CV)0.51187518
Kurtosis0.34123233
Mean9.5749668
Median Absolute Deviation (MAD)3.45234
Skewness0.44100137
Sum2816122.2
Variance24.021642
MonotonicityNot monotonic
2024-07-08T20:33:45.319166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
6.90468 29910
10.1%
8.05546 28613
9.7%
9.20624 26832
9.1%
5.7539 26602
9.0%
10.35702 24161
 
8.2%
11.5078 23227
 
7.9%
4.60312 21002
 
7.1%
12.65858 19392
 
6.6%
13.80936 16082
 
5.5%
3.45234 15822
 
5.4%
Other values (18) 62470
21.2%
ValueCountFrequency (%)
0 14345
4.9%
3.45234 15822
5.4%
4.60312 21002
7.1%
5.7539 26602
9.0%
6.7090474 17
 
< 0.1%
6.90468 29910
10.1%
8.05546 28613
9.7%
9.20624 26832
9.1%
10.35702 24161
8.2%
11.5078 23227
7.9%
ValueCountFrequency (%)
32.22184 157
 
0.1%
31.07106 7
 
< 0.1%
29.92028 17
 
< 0.1%
28.7695 130
 
< 0.1%
27.61872 109
 
< 0.1%
26.46794 204
 
0.1%
25.31716 525
 
0.2%
24.16638 637
 
0.2%
23.0156 932
0.3%
21.86482 1795
0.6%

wind_gust
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing783
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean11.01868
Minimum0
Maximum37.080249
Zeros14345
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:45.403059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.9728838
Q16.621473
median10.594357
Q314.567241
95-th percentile21.188714
Maximum37.080249
Range37.080249
Interquartile range (IQR)7.9457677

Descriptive statistics

Standard deviation5.6401889
Coefficient of variation (CV)0.51187518
Kurtosis0.34123233
Mean11.01868
Median Absolute Deviation (MAD)3.9728838
Skewness0.44100137
Sum3240737.1
Variance31.811731
MonotonicityNot monotonic
2024-07-08T20:33:45.486689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7.94576765 29910
10.1%
9.270062259 28613
9.7%
10.59435687 26832
9.1%
6.621473042 26602
9.0%
11.91865148 24161
 
8.2%
13.24294608 23227
 
7.9%
5.297178434 21002
 
7.1%
14.56724069 19392
 
6.6%
15.8915353 16082
 
5.5%
3.972883825 15822
 
5.4%
Other values (18) 62470
21.2%
ValueCountFrequency (%)
0 14345
4.9%
3.972883825 15822
5.4%
5.297178434 21002
7.1%
6.621473042 26602
9.0%
7.720637567 17
 
< 0.1%
7.94576765 29910
10.1%
9.270062259 28613
9.7%
10.59435687 26832
9.1%
11.91865148 24161
8.2%
13.24294608 23227
7.9%
ValueCountFrequency (%)
37.08024904 157
 
0.1%
35.75595443 7
 
< 0.1%
34.43165982 17
 
< 0.1%
33.10736521 130
 
< 0.1%
31.7830706 109
 
< 0.1%
30.45877599 204
 
0.1%
29.13448138 525
 
0.2%
27.81018678 637
 
0.2%
26.48589217 932
0.3%
25.16159756 1795
0.6%

visib
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing783
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean9.3629116
Minimum0.12
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-07-08T20:33:45.572509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile4
Q110
median10
Q310
95-th percentile10
Maximum10
Range9.88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8693639
Coefficient of variation (CV)0.19965626
Kurtosis8.8636494
Mean9.3629116
Median Absolute Deviation (MAD)0
Skewness-3.1135996
Sum2753754
Variance3.4945213
MonotonicityNot monotonic
2024-07-08T20:33:45.649291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 253672
86.0%
9 6478
 
2.2%
8 4843
 
1.6%
4 4797
 
1.6%
6 4739
 
1.6%
7 4233
 
1.4%
5 3807
 
1.3%
3 3462
 
1.2%
2 1876
 
0.6%
2.5 1613
 
0.5%
Other values (11) 4593
 
1.6%
ValueCountFrequency (%)
0.12 17
 
< 0.1%
0.13 82
 
< 0.1%
0.24 93
 
< 0.1%
0.25 416
0.1%
0.5 778
0.3%
0.75 553
0.2%
1 713
0.2%
1.25 234
 
0.1%
1.5 823
0.3%
1.75 427
0.1%
ValueCountFrequency (%)
10 253672
86.0%
9 6478
 
2.2%
8 4843
 
1.6%
7 4233
 
1.4%
6 4739
 
1.6%
5 3807
 
1.3%
4 4797
 
1.6%
3.5 457
 
0.2%
3 3462
 
1.2%
2.5 1613
 
0.5%

Interactions

2024-07-08T20:33:30.960562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:15.602101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.874494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:27.704340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:12.650582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.645583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:24.258181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:31.008524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.669451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:42.274528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.771942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:53.155965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.958925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.644531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:11.124917image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:17.774880image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:23.473108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:31.053797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:15.714597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.958927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:30.172096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:12.754410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.746562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:24.370168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:31.103272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.758798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:42.372052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.857198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:53.247112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:00.067062image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.729146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:11.274293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:17.864513image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:23.562611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:31.200662image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:15.872001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:22.103194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:32.481143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:12.921269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.903424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:24.530095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:31.247878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.912919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:42.522934image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:48.001838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:54.675647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:00.208629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.876520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:11.478556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:18.013591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:23.715250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:35.146218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:20.499809image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-07-08T20:32:29.566275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:35.122951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:40.907687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:46.370548image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:51.854758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:58.538478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:04.272355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:09.760693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:15.446860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:22.019375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:29.381233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:35.248382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:20.604691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:26.334289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:40.906931image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:17.243412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:22.949111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:29.668439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:35.233379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.007653image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:46.473254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:51.952941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:58.646452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:04.373736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:09.856290image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:15.605033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:22.123945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:29.493498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:35.339083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:20.697793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:26.424891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:43.418296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:17.353624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.047045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:29.762998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:35.326960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.101822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:46.568638image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.045888image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:58.745073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:04.467275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:09.953329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:15.762411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:22.223918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:29.598244image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:35.425886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:20.795591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:26.518393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:45.677891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:17.464017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.142329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:29.870838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:35.423208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.202464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:46.668781image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-07-08T20:32:58.840406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-07-08T20:33:29.924868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-07-08T20:31:52.856577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:17.778347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.437691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.170875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:35.805533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.473405image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:46.959910image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.407943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.132140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:04.861060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.318936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:16.390963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:22.597137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.024893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:35.776965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.169519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:26.906235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:55.114615image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:17.872274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.533560image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.262189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:35.908152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.559666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.049339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.484966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.217705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:04.957428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.400962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:16.540976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:22.684987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.113046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:35.859181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.259421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:26.999470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:57.758525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:17.969894image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.624845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.355293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.019928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.653839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.137806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.568186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.311684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.050679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.486542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:16.697734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:22.780655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.212407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:35.945802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.355982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:27.101104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:00.046649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.081291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.718069image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.459257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.133122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.752360image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.234573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.661795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.407959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.142067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.578125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:16.857807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:22.884741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.309953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:36.086429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.497083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:27.245832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:02.364313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.231922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.865021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.603459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.284139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.897081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.388256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.801431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.558225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.281014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.715933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:17.048845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:23.033549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.474155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:36.246406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.593710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:27.347380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:04.845002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.338849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:23.965507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.704667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.383239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:41.995936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.486629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.892591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.656658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.372042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.803900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:17.206204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:23.200273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.666407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:36.340516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.691453image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:27.449364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:07.197857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.444087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:24.070741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.803462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.481186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:42.084380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.582075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:52.979489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.763261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.460941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.892989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:17.426613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:23.295607image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.759520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:36.433124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:21.783936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:31:27.551592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:10.333341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:18.548987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:24.167265image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:30.912354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:36.577321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:42.177614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:47.681394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:53.068474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:32:59.874182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:05.554233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:10.987315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:17.635702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:23.385720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-07-08T20:33:30.861372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Missing values

2024-07-08T20:33:36.708037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-08T20:33:37.516752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-08T20:33:38.731518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

monthdaydep_timedep_delayarr_delaycarrierflighttailnumorigindestair_timedistancefaanamelatlonaltyear_planestypemanufacturermodelenginesseatsenginedest_datetimewind_dirwind_speedwind_gustvisib
01010027193.0182.0UA1537N54711EWRMIA148.01085.0MIAMiami International Airport25.793200-80.2906048.01998.0Fixed wing multi engineBOEING737-7242.0149.0Turbo-fan2017-10-02 00:00:0040.09.2062410.59435710.0
11010519-6.0-18.0UA1161N37287EWRIAH183.01400.0IAHGeorge Bush Intercontinental Houston Airport29.984400-95.34140097.02004.0Fixed wing multi engineBOEING737-8242.0149.0Turbo-fan2017-10-01 05:00:00360.016.1109218.54012510.0
21010544-1.0-4.0B627N547JBEWRMCO133.0937.0MCOOrlando International Airport28.429399-81.30899896.02002.0Fixed wing multi engineAIRBUSA320-2322.0200.0Turbo-fan2017-10-01 05:00:00360.016.1109218.54012510.0
31010546-4.0-17.0UA761N38446LGAORD108.0733.0ORDChicago O'Hare International Airport41.978600-87.904800672.02012.0Fixed wing multi engineBOEING737-924ER2.0191.0Turbo-fan2017-10-01 05:00:00360.019.5632622.51300810.0
41010552-8.0-4.0NK521N675NKEWRFLL156.01065.0FLLFort Lauderdale Hollywood International Airport26.072599-80.1527029.02017.0Fixed wing multi engineAIRBUSA321-2312.0379.0Turbo-fan2017-10-01 05:00:00360.016.1109218.54012510.0
51010553-7.0-6.0B623N967JBJFKLAX326.02475.0LAXLos Angeles International Airport33.942501-118.407997125.0NaNNaNNaNNaNNaNNaNNaN2017-10-01 05:00:00340.021.8648225.16159810.0
61010554-6.00.0AA129N3GMAALGAMCO139.0950.0MCOOrlando International Airport28.429399-81.30899896.0NaNNaNNaNNaNNaNNaNNaN2017-10-01 05:00:00360.019.5632622.51300810.0
71010555-12.0-22.0UA1465N17229EWRDFW187.01372.0DFWDallas Fort Worth International Airport32.896801-97.038002607.01999.0Fixed wing multi engineBOEING737-8242.0149.0Turbo-fan2017-10-01 05:00:00360.016.1109218.54012510.0
81010555-5.0-21.0AA1301N3BMAAEWRMIA143.01085.0MIAMiami International Airport25.793200-80.2906048.0NaNNaNNaNNaNNaNNaNNaN2017-10-01 05:00:00360.016.1109218.54012510.0
91010556-4.0-13.0DL575N932DLEWRATL103.0746.0ATLHartsfield Jackson Atlanta International Airport33.636700-84.4281011026.01989.0Fixed wing multi engineMCDONNELL DOUGLAS AIRCRAFT COMD-882.0142.0Turbo-fan2017-10-01 05:00:00360.016.1109218.54012510.0
monthdaydep_timedep_delayarr_delaycarrierflighttailnumorigindestair_timedistancefaanamelatlonaltyear_planestypemanufacturermodelenginesseatsenginedest_datetimewind_dirwind_speedwind_gustvisib
2948869302152-10.0-40.0B6985N239JBJFKRDU64.0427.0RDURaleigh Durham International Airport35.877602-78.787498435.02006.0Fixed wing multi engineEMBRAERERJ 190-100 IGW2.020.0Turbo-fan2017-09-30 21:00:00340.016.1109218.54012510.0
2948879302152-7.0-20.0UA2062N66808EWRBOS36.0200.0BOSGeneral Edward Lawrence Logan International Airport42.364300-71.00520320.02013.0Fixed wing multi engineBOEING737-924ER2.0191.0Turbo-fan2017-09-30 21:00:00330.016.1109218.54012510.0
29488893021550.00.0B6127N729JBEWRMCO129.0937.0MCOOrlando International Airport28.429399-81.30899896.0NaNFixed wing multi engineAIRBUSA320-2322.0200.0Turbo-fan2017-09-30 21:00:00330.016.1109218.54012510.0
2948899302231-9.0-21.0B6486N228JBJFKROC49.0264.0ROCGreater Rochester International Airport43.118900-77.672401559.02006.0Fixed wing multi engineEMBRAERERJ 190-100 IGW2.020.0Turbo-fan2017-09-30 22:00:00340.014.9601417.21583010.0
2948909302235-5.0-17.0B6108N249JBJFKPWM53.0273.0PWMPortland International Jetport Airport43.646198-70.30930376.02006.0Fixed wing multi engineEMBRAERERJ 190-100 IGW2.020.0Turbo-fan2017-09-30 22:00:00340.014.9601417.21583010.0
2948919302238-7.0-25.0B61816N318JBJFKSYR42.0209.0SYRSyracuse Hancock International Airport43.111198-76.106300421.02010.0Fixed wing multi engineEMBRAERERJ 190-100 IGW2.020.0Turbo-fan2017-09-30 22:00:00340.014.9601417.21583010.0
2948929302241-4.0-19.0B62002N346JBJFKBUF54.0301.0BUFBuffalo Niagara International Airport42.940498-78.732201728.02011.0Fixed wing multi engineEMBRAERERJ 190-100 IGW2.020.0Turbo-fan2017-09-30 22:00:00340.014.9601417.21583010.0
29489393022483.0-9.0F9509N238FRLGADEN213.01620.0DENDenver International Airport39.861698-104.6729975431.02016.0Fixed wing multi engineAIRBUSA320-2142.0182.0Turbo-fan2017-09-30 22:00:00340.013.8093615.89153510.0
29489493023192.0-10.0B6718N296JBJFKBOS37.0187.0BOSGeneral Edward Lawrence Logan International Airport42.364300-71.00520320.02008.0Fixed wing multi engineEMBRAERERJ 190-100 IGW2.020.0Turbo-fan2017-09-30 23:00:00330.016.1109218.54012510.0
294895930232549.035.0B6234N184JBJFKBTV48.0266.0BTVBurlington International Airport44.471901-73.153297335.02005.0Fixed wing multi engineEMBRAERERJ 190-100 IGW2.020.0Turbo-fan2017-09-30 23:00:00330.016.1109218.54012510.0